Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604720
V. Chobanov, Mihaela Asenova
In this case study will be summarised the benefits of upgrading existing building services systems, and more specifically the existing electrical systems. The advantages will be reviewed in following aspects: Economical; Energy consumption; Human Comfort; Reducing Carbon Footprint. For the purposes of this report, an existing building will be reviewed where refurbishment has been undergone in its design stage for the building services systems. The aim of the report will be to evaluate the systems changes and their impact to the building overall performance, and also provide suggestions for further improvements to be implemented.
{"title":"Evaluation of an electrical infrastructure of an existing building and economic and environmental benefits of its modernization","authors":"V. Chobanov, Mihaela Asenova","doi":"10.1109/ISMSIT52890.2021.9604720","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604720","url":null,"abstract":"In this case study will be summarised the benefits of upgrading existing building services systems, and more specifically the existing electrical systems. The advantages will be reviewed in following aspects: Economical; Energy consumption; Human Comfort; Reducing Carbon Footprint. For the purposes of this report, an existing building will be reviewed where refurbishment has been undergone in its design stage for the building services systems. The aim of the report will be to evaluate the systems changes and their impact to the building overall performance, and also provide suggestions for further improvements to be implemented.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123259700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604563
Tsvetelina Mladenova
The K-nearest Neighbor algorithm is a well-known non-parametric algorithm used for classifying. The algorithm is a simple, intuitive and preferable choice for many machine-learning models. Having that in mind, the negatives of the method should not be overlooked – the sensitivity of the k value, the choosing method of the neighbors and the voting mechanism.This paper reviews some state-of-art weight algorithms and motivated by their ideas proposes a solution for weight function. Unlike most weight functions, the proposed solution uses the features of the neighbors instead of just their distances. Some experiments are conducted on both real-world datasets and on well-known experimental ones. Some future improvements are targeted and the advantages and disadvantages are discussed.
{"title":"A Feature-Weighted Rule for the K-Nearest Neighbor","authors":"Tsvetelina Mladenova","doi":"10.1109/ISMSIT52890.2021.9604563","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604563","url":null,"abstract":"The K-nearest Neighbor algorithm is a well-known non-parametric algorithm used for classifying. The algorithm is a simple, intuitive and preferable choice for many machine-learning models. Having that in mind, the negatives of the method should not be overlooked – the sensitivity of the k value, the choosing method of the neighbors and the voting mechanism.This paper reviews some state-of-art weight algorithms and motivated by their ideas proposes a solution for weight function. Unlike most weight functions, the proposed solution uses the features of the neighbors instead of just their distances. Some experiments are conducted on both real-world datasets and on well-known experimental ones. Some future improvements are targeted and the advantages and disadvantages are discussed.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114250087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604749
Anurag Dasgupta
Distributed algorithms are now present in many computing activities. The price decline of computer hardware, increase in speed of communication, the versatility of the Internet, and the advancement of network technology all have contributed immensely to the incredible growth and importance of distributed computing. Therefore, even an undergraduate is now expected to know at least the basics of distributed algorithms. This paper describes the experience and performance of students who are introduced to distributed algorithms in an undergraduate Algorithms course. The paper shows that for teaching new concepts, providing in-class examples should be emphasized more for effective student learning. It is more important compared to prior knowledge of similar concepts.
{"title":"Student Performance Evaluation of Distributed Algorithms Assignment in an Undergraduate Algorithms Course","authors":"Anurag Dasgupta","doi":"10.1109/ISMSIT52890.2021.9604749","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604749","url":null,"abstract":"Distributed algorithms are now present in many computing activities. The price decline of computer hardware, increase in speed of communication, the versatility of the Internet, and the advancement of network technology all have contributed immensely to the incredible growth and importance of distributed computing. Therefore, even an undergraduate is now expected to know at least the basics of distributed algorithms. This paper describes the experience and performance of students who are introduced to distributed algorithms in an undergraduate Algorithms course. The paper shows that for teaching new concepts, providing in-class examples should be emphasized more for effective student learning. It is more important compared to prior knowledge of similar concepts.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"7 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114548872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604740
Chi Wei Foo, Lee Choo Tay, W. Lai
Quality control is an essential component in manufacturing process that ensures defective products are not shipped to customers. For a product like wiper arms, sophisticated methods for defect detection are required due to challenges of complex structures and reflective surfaces. This paper aimed to show enhancement to the previous solution by improving the image enhancement and thresholding steps, as well as the defect detection algorithm applied. In this proposed approach, the image is segmented using an improved Otsu-based threshold value selection to acquire the region of interest (ROI). The image enhancement step utilizes a modified iterative Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) that evaluates the clip limit required via contrast and entropy of images. This enhanced image is then binarized via adaptive thresholding with probabilistic Hough transform (PHT) and convex hull algorithm to preserve the car wiper arm features. The defect is then detected using a multistage blob detection algorithm that involves blob contour analysis based on contour area and contour mean values. Test results showed significant reduction in false negatives from 52% to 12.4%.
{"title":"Adaptive Thresholding with Iterative Fuzzy Logic Based Image Enhancements for Wiper Arm Defect Detection","authors":"Chi Wei Foo, Lee Choo Tay, W. Lai","doi":"10.1109/ISMSIT52890.2021.9604740","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604740","url":null,"abstract":"Quality control is an essential component in manufacturing process that ensures defective products are not shipped to customers. For a product like wiper arms, sophisticated methods for defect detection are required due to challenges of complex structures and reflective surfaces. This paper aimed to show enhancement to the previous solution by improving the image enhancement and thresholding steps, as well as the defect detection algorithm applied. In this proposed approach, the image is segmented using an improved Otsu-based threshold value selection to acquire the region of interest (ROI). The image enhancement step utilizes a modified iterative Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) that evaluates the clip limit required via contrast and entropy of images. This enhanced image is then binarized via adaptive thresholding with probabilistic Hough transform (PHT) and convex hull algorithm to preserve the car wiper arm features. The defect is then detected using a multistage blob detection algorithm that involves blob contour analysis based on contour area and contour mean values. Test results showed significant reduction in false negatives from 52% to 12.4%.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604721
Selim Buyrukoğlu
Cryptocurrency is in great demand today and there is pretty much investment in cryptocurrencies by the investors. There are more than 6000 cryptocurrencies all over the world, which clearly shows that cryptocurrency is a growing investment market. For this reason, investors having ordinary income invest in promising cryptocurrencies with a low market value. However, these investors are often unconsciously investing and making losses. At this point, sensible investments can be made using data analysis methods based on deep learning. Therefore, this study aims to analyze promising cryptocurrencies with deep learning methods. Five promising cryptocurrencies were analyzed with the ensembles of LSTM and single-based LSTM networks. This study revealed that ensembles of LSTM network do not always provide better accuracy performance than the single-based LSTM network in the analysis of promising cryptocurrencies. In other words, these two deep learning methods can be employed to obtain reliable analysis results in promising cryptocurrencies.
{"title":"Promising Cryptocurrency Analysis using Deep Learning","authors":"Selim Buyrukoğlu","doi":"10.1109/ISMSIT52890.2021.9604721","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604721","url":null,"abstract":"Cryptocurrency is in great demand today and there is pretty much investment in cryptocurrencies by the investors. There are more than 6000 cryptocurrencies all over the world, which clearly shows that cryptocurrency is a growing investment market. For this reason, investors having ordinary income invest in promising cryptocurrencies with a low market value. However, these investors are often unconsciously investing and making losses. At this point, sensible investments can be made using data analysis methods based on deep learning. Therefore, this study aims to analyze promising cryptocurrencies with deep learning methods. Five promising cryptocurrencies were analyzed with the ensembles of LSTM and single-based LSTM networks. This study revealed that ensembles of LSTM network do not always provide better accuracy performance than the single-based LSTM network in the analysis of promising cryptocurrencies. In other words, these two deep learning methods can be employed to obtain reliable analysis results in promising cryptocurrencies.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123491562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604578
S. Coskun, Omer Ozgur
The semi-active suspension systems equipped with a magnetorheological (MR) damper are extensively used due to their improved performance in vibration reduction. Supplying proper voltage as an input, the MR fluid behavior will form a solid-state, controlling stiffness and damping in semi-active suspensions. Incorporating the benefits of active suspension and passive suspension, a semi-active suspension with an MR damper offers a mechanically simple, safe, and low power consumption solution. This study develops an extended fuzzy control strategy to improve ride control under road disturbances. To this aim, a quarter car model is adopted where the modified Bouc-Wen MR damper model is utilized to control damping force via a fuzzy control and a proportional-integral control. Several simulations are carried out with the designed controller under both bump and sinusoidal types of road disturbances. The merits of the proposed controller are compared with a classical fuzzy controller and uncontrolled case, wherein a significant improvement in reducing body displacement as well as suspension deflections is achieved.
{"title":"Hybrid Fuzzy Control of Semi-Active Suspension System Using Magnetorheological Damper","authors":"S. Coskun, Omer Ozgur","doi":"10.1109/ISMSIT52890.2021.9604578","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604578","url":null,"abstract":"The semi-active suspension systems equipped with a magnetorheological (MR) damper are extensively used due to their improved performance in vibration reduction. Supplying proper voltage as an input, the MR fluid behavior will form a solid-state, controlling stiffness and damping in semi-active suspensions. Incorporating the benefits of active suspension and passive suspension, a semi-active suspension with an MR damper offers a mechanically simple, safe, and low power consumption solution. This study develops an extended fuzzy control strategy to improve ride control under road disturbances. To this aim, a quarter car model is adopted where the modified Bouc-Wen MR damper model is utilized to control damping force via a fuzzy control and a proportional-integral control. Several simulations are carried out with the designed controller under both bump and sinusoidal types of road disturbances. The merits of the proposed controller are compared with a classical fuzzy controller and uncontrolled case, wherein a significant improvement in reducing body displacement as well as suspension deflections is achieved.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130221237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604681
M. Ertem, V. Bier
We propose a general defender-attacker model for security of computer networks, using attack graphs to represent the possible attacker strategies and defender options. The defender’s objective is to maximize the security of the network under a limited budget. In computer security, the attacker could be operating from the safety of a foreign country, and the cost of changing attack strategies may be quite low. To capture the ability of the attacker to launch multiple attempts, we represent the attacker’s success on each arc of the attack graph probabilistically, and formulate the resulting problem as a two- stage stochastic NI problem. We solve the resulting problem using two-stage stochastic optimization with recourse, and explore the attacker and defender strategies.
{"title":"A Stochastic Network-Interdiction Model For Cyber Security","authors":"M. Ertem, V. Bier","doi":"10.1109/ISMSIT52890.2021.9604681","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604681","url":null,"abstract":"We propose a general defender-attacker model for security of computer networks, using attack graphs to represent the possible attacker strategies and defender options. The defender’s objective is to maximize the security of the network under a limited budget. In computer security, the attacker could be operating from the safety of a foreign country, and the cost of changing attack strategies may be quite low. To capture the ability of the attacker to launch multiple attempts, we represent the attacker’s success on each arc of the attack graph probabilistically, and formulate the resulting problem as a two- stage stochastic NI problem. We solve the resulting problem using two-stage stochastic optimization with recourse, and explore the attacker and defender strategies.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604638
B. Kostov, V. Hristov
In the present work, an implementation of 3D measuring sensor for calibrating user coordinate systems of robot is presented. The implementation is done by specially developed algorithm for the purpose. The algorithm is fully functional as for robots and other manipulators. This algorithm is developed in simulation mode and implemented in real environment. The calibration method is working on 100%
{"title":"Implementation of 3D measuring sensor for callibrating robot coordinate systems","authors":"B. Kostov, V. Hristov","doi":"10.1109/ISMSIT52890.2021.9604638","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604638","url":null,"abstract":"In the present work, an implementation of 3D measuring sensor for calibrating user coordinate systems of robot is presented. The implementation is done by specially developed algorithm for the purpose. The algorithm is fully functional as for robots and other manipulators. This algorithm is developed in simulation mode and implemented in real environment. The calibration method is working on 100%","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126861421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604677
A. Alnemer, Jawad Rasheed
Brain tumor is ranked 12th deadliest cancerous tumor among children and adults. An early detection and identification of its type can help radiologists and medical practitioners in defining an effective treatment. Therefore, this study aims to devise an efficient but accurate human braintumor classification tool that exploits magnetic resonance imaging (MRI) to segregate glioma, meningioma, pituitary, and no tumor. For this, the study exploits a dataset of 7023 MR images and performed various image pro-processing steps to get brain image by removing unwanted noisy areas and margins. Further, it incorporates data augmentation techniques to enhance limited dataset and avoid overfitting issue. Finally, a modified pre-trained deep learning-based ResNet152V2 model is trained. Two separate experiments are conducted by training proposed model with and without augmented data. It is observed that the proposed network trained on augmented data significantly outperformed the network trained on original data by successfully distinguishing four clinical states of brain tumor with an overall accuracy of 98.9%.
{"title":"An Efficient Transfer Learning-based Model for Classification of Brain Tumor","authors":"A. Alnemer, Jawad Rasheed","doi":"10.1109/ISMSIT52890.2021.9604677","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604677","url":null,"abstract":"Brain tumor is ranked 12th deadliest cancerous tumor among children and adults. An early detection and identification of its type can help radiologists and medical practitioners in defining an effective treatment. Therefore, this study aims to devise an efficient but accurate human braintumor classification tool that exploits magnetic resonance imaging (MRI) to segregate glioma, meningioma, pituitary, and no tumor. For this, the study exploits a dataset of 7023 MR images and performed various image pro-processing steps to get brain image by removing unwanted noisy areas and margins. Further, it incorporates data augmentation techniques to enhance limited dataset and avoid overfitting issue. Finally, a modified pre-trained deep learning-based ResNet152V2 model is trained. Two separate experiments are conducted by training proposed model with and without augmented data. It is observed that the proposed network trained on augmented data significantly outperformed the network trained on original data by successfully distinguishing four clinical states of brain tumor with an overall accuracy of 98.9%.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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